https://github.com/enyaude/integrated-project-maji-ndogo-part-2

Explore Maji Ndogo's water quality analysis project, where a detailed 60,000-record database is used to identify pollution hotspots and guide intervention strategies. Leveraging Python, MySQL, and Jupyter Notebook, this project uncovers patterns and addresses environmental issues, driven by the call for data-driven solutions by President Naledi.
https://github.com/enyaude/integrated-project-maji-ndogo-part-2

jupyter-notebook mysql mysql-database python sql

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Explore Maji Ndogo's water quality analysis project, where a detailed 60,000-record database is used to identify pollution hotspots and guide intervention strategies. Leveraging Python, MySQL, and Jupyter Notebook, this project uncovers patterns and addresses environmental issues, driven by the call for data-driven solutions by President Naledi.

• Host: GitHub
• URL: https://github.com/enyaude/integrated-project-maji-ndogo-part-2
• Owner: Enyaude
• Created: 2024-08-19T03:22:05.000Z (5 months ago)
• Default Branch: master
• Last Pushed: 2024-08-19T03:56:40.000Z (5 months ago)
• Last Synced: 2024-10-16T19:26:11.559Z (3 months ago)
• Topics: jupyter-notebook, mysql, mysql-database, python, sql
• Language: Jupyter Notebook
• Homepage:
• Size: 35.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Project Title: Maji Ndogo - Water Quality Analysis 2

Project Overview:

This project focuses on analyzing water quality data to identify pollution hotspots and develop strategies for intervention. Using a robust dataset of 60,000 records from Maji Ndogo, we aim to uncover patterns and provide data-driven solutions.

Goal: Analyze water quality data to pinpoint pollution hotspots and guide effective intervention strategies.

Data: The dataset includes key tables such as data_dictionary, employee, global_water_access, location, water_quality, visits, water_source, and well_pollution.

Tools: SQL, Python, Jupyter Notebook.

Process:

Data Preparation: Clean and preprocess the data by handling missing values and outliers.

Data Analysis: Conduct analysis using SQL within Jupyter Notebook to identify crucial correlations and insights.

Guided by SQL and Python, this analysis dives deep into the data, revealing the critical information needed to address Maji Ndogo’s water crisis. Inspired by President Naledi’s call for data-driven solutions, this project is a step towards securing a healthier future through informed decision-making.